Open pascalwhoop opened 1 month ago
a workaround I'm using is pass the count query by read_options["query.count"] = "MATCH (n: Entity) RETURN COUNT(*) as count"
, it then uses the whatever meta store to get node count in O(1). This is especially needed when scanning edges, because that store only supports specifying edge type + 1 node type for O1 access
Additionally, the connector will convert partition into SKIP+LIMIT(where each executor still need to SKIP then read LIMIT.........), and the dataframe will contain duplicates when your db is also under writes
Interessant thanks for Sharing that. We ended up filtering using the Labels parameter instead and got better results that way. I guess at the moment the queries are treated as a string not decomposed into a query that can be optimized by a query planner as eg spark does it
On Sat, 27 Jul 2024 at 06:52, ahxxm @.***> wrote:
a workaround I'm using is pass the count query by read_options["query.count"] = "MATCH (n: Entity) RETURN COUNT(*) as count", it then uses the whatever meta store to get node count in O(1). This is especially needed when scanning edges, because that store only supports specifying edge type + 1 node type for O1 access
Additionally, the connector will convert partition into SKIP+LIMIT, and the dataframe will contain duplicates when your db is also under writes
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yeah the connector just wraps simple operations without heuristics.. by "labels parameter" do you mean some indexed fields that enable a cursur-based query?
I guess at the moment the queries are treated as a string not decomposed into a query that can be optimized by a query planner as eg spark does it
That's correct, the Spark connector currently treats Cypher queries as black boxes.
Expected Behavior (Mandatory)
partitions: 8 should not lead to performance degradation upon read https://neo4j.com/docs/spark/current/read/options/
Actual Behavior (Mandatory)
The query
is converted into
Which means it retrieves all data from DB, then counts them, then calculates partitions size and reads again rather than directly doing a
count(*)
(which is much faster).is this because we're using the spark 'query' based reading?